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    We introduce a low-complexity Convolutional Long Short-Term Memory (ConvLSTM) for hyperspectral image classification. Our tensorized approach significantly reduces model size and computation while maintaining high accuracy.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Remote Sensing

    Background:

    • Convolutional Long Short-Term Memory (ConvLSTM) excels at spatial-spectral analysis in hyperspectral imaging.
    • High model complexity hinders ConvLSTM deployment in resource-limited scenarios.

    Purpose of the Study:

    • To develop a computationally efficient ConvLSTM for hyperspectral image (HSI) classification.
    • To reduce model complexity without sacrificing classification performance.

    Main Methods:

    • Proposed a novel tensor-sequenced convolution (ETTConv) using tensor train (TT) format.
    • Developed a lightweight ETTConvLSTM unit by compressing weight tensors.
    • Constructed 2-D (ETTCL2DNN) and 3-D (ETTCL3DNN) neural network models based on the ETTConvLSTM unit.

    Main Results:

    • ETTConv significantly reduces parameters and computations compared to standard convolutions.
    • The proposed ETTCL2DNN and ETTCL3DNN models achieve reduced complexity.
    • Experimental results show competitive or improved classification performance on HSI datasets.

    Conclusions:

    • The proposed tensorized ConvLSTM models offer a viable solution for efficient HSI classification.
    • ETTConv and ETTConvLSTM provide a pathway to deploy advanced deep learning models in resource-constrained environments.
    • The 3-D version effectively preserves joint spatial-spectral information for enhanced classification.